AI Networking for the Campus LAN: Navigating the New Frontier

The campus LAN, encompassing both wired and wireless networks, has long been a bastion of stability within the ever-evolving enterprise IT ecosystem. While much of IT has undergone radical transformations over the past few decades, the campus LAN's operation and architecture have largely remained unchanged over the last 30+ years.

However, this is poised to change as artificial intelligence (AI) emerges as a transformative force in this mature market segment. IT leaders, already grappling with a shortage of engineering talent and resources, are increasingly turning to AI-driven solutions to alleviate the burden of everyday administrative tasks and prioritize more pressing technology projects.

Defining AI in Networking

The benefits of implementing AI in networking are manifold. Network administrators can achieve unprecedented efficiency by leveraging AI-driven analytics, automation, and predictive maintenance. AI can analyze vast amounts of telemetry data in real-time, identify patterns, and make intelligent decisions toward improving overall system health that would be impossible for IT admins to achieve manually. This leads to faster troubleshooting and proactive issue resolution, enabling IT leaders to focus more time and personnel on higher-profile projects.

AI networking can be classified into different levels based on the degree of automation and intelligence involved. At the most basic level, AI can be used for monitoring and reporting, providing network administrators with insights and recommendations based on data analysis. As AI systems become more advanced, they can take on more complex tasks, such as proactive monitoring, automated troubleshooting, and continuous performance optimization.

IT leaders should expect certain capabilities from AI networking solutions. These include real-time anomaly detection, automated root cause analysis, closed-loop remediation, and dynamic network optimization. AI should also play a role in enhancing network security by identifying and mitigating threats faster than traditional methods.

However, it is crucial to note that AI should not be seen as a panacea for all network challenges. For example, IT should avoid using AI to resolve legacy technology shortcomings, such as pointing out missing VLANs or finding the best software to run in an Access Point. Instead, AI should assist IT staff and provide guidance that is transparent to end-users as well.

IT Considerations About AI Networking and AIOps

Despite AI networking's promise, IT leaders must address multiple considerations before implementing it. Some of these considerations include:

AI Should Be “Baked In" to a Network Architecture

Campus LANs, especially those in large organizations, are complex ecosystems that may not be readily compatible with AI point solutions.

IT leaders should look to architectures that natively incorporate AI technologies rather than take a more generic AI tool and apply it to their individual networks. Moreover, having a single AI solution that comprehensively assists with both wired and wireless portions of the network is key. Using add-on solutions could create conflicts, as these disparate solutions won’t necessarily be in sync and can produce conflicting observances and recommendations.

AI Should Provide Solutions, Not Just Identify Problems

Particularly for the wired and wireless LAN, IT needs a deterministic AI networking architecture, as opposed to one that is probabilistic (meaning it just gives you an answer that is likely to be correct versus one that is definitive).

Many AIOps solutions will point out something is wrong with the network but leave efforts to fix these problems to the IT staff. The real power of AI networking is that it should automate issue resolution, not just identification. IT leaders should instead look at leveraging AI for comprehensive data collection to go beyond AI-generated report summaries and completely automate the traditionally manual lifecycle of enterprise networks.

Eliminate Potential Limits on AI Performance

AI should not be limited by manually set service level expectations. Instead, automation should adjust to dynamic baselines, allowing the network to adapt to changing conditions in real time. Additionally, AI assistants should not come at an extra cost just to summarize reports and dashboards but should be part of the standard offering to automate actions.

Enable Shareable Observability

AI telemetry should provide insights that are not only useful for a single vendor's solutions but also shareable with integration partners for enhanced visibility and action.

Building an AI Networking Strategy

In conclusion, AI networking represents a significant opportunity for IT leaders to enhance their campus LANs' performance, security, and efficiency, in many cases enabling IT to automate lifecycle management at scale.

However, careful consideration must be given to the importance of having a cohesive AI strategy that integrates seamlessly across all wired and wireless network segments towards closed-loop automation rather than simple AI summaries of network management reports. By approaching AI networking with a strategic mindset that demands more from the technology vendors, IT departments can fully leverage the benefits of this transformative technology.